{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import train_test_split\n",
    "pd.set_option('display.max_columns',1000)\n",
    "\n",
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "\n",
    "data=df[['Age','Fare']]\n",
    "target = df['Survived']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(data.values, target.values, test_size=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,\n",
       "       max_depth=3, min_child_weight=1, missing=None, n_estimators=100,\n",
       "       n_jobs=1, nthread=None, objective='binary:logistic', random_state=0,\n",
       "       reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
       "       silent=True, subsample=1)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = XGBClassifier()\n",
    "clf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_preds = clf.predict_proba(X_test)\n",
    "preds = y_preds[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/tYlFKekM79OcpnWrARAhwjndm9IoOsrP0QW4JUvgyith7lwYOtQK2Jmw5s3w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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdaf84246a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fpr means false-positive-rate\n",
    "# tpr means true-positive-rate\n",
    "fpr, tpr, _ = metrics.roc_curve(y_test, preds)\n",
    "\n",
    "auc_score = metrics.auc(fpr, tpr)\n",
    "\n",
    "plt.title('ROC Curve')\n",
    "plt.plot(fpr, tpr, label='AUC = {:.3f}'.format(auc_score))\n",
    "\n",
    "# it's helpful to add a diagonal to indicate where chance \n",
    "# scores lie (i.e. just flipping a coin)\n",
    "plt.plot([0,1],[0,1],'r--')\n",
    "\n",
    "plt.xlim([-0.1,1.1])\n",
    "plt.ylim([-0.1,1.1])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "importance_data = sorted(list(zip(data.columns,clf.feature_importances_)),key=lambda tpl:tpl[1],reverse=True)\n",
    "\n",
    "xs = range(len(importance_data))\n",
    "labels = [x for (x,_) in importance_data]\n",
    "\n",
    "ys = [y for (_,y) in importance_data]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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INUvalJU4t2+Ll/WB2ohLKuOfA7XxuNu3xdNktlmzKp7XlnLn6k0ReyrjzzpY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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdab0c07c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "plt.bar(xs,ys,width=0.5)\n",
    "plt.xticks(xs,labels)\n",
    "\n",
    "plt.gca().grid(True)\n",
    "# select both y axis and x axis\n",
    "gridlines = plt.gca().get_xgridlines() + plt.gca().get_ygridlines()\n",
    "# choose line width\n",
    "line_width = 0.7\n",
    "\n",
    "plt.ylabel('relative feature importance')\n",
    "\n",
    "for line in gridlines:\n",
    "    line.set_linestyle(':')\n",
    "    line.set_linewidth(line_width)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### all variables, including categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df[['Pclass','Sex','SibSp','Embarked','Age','Fare','Survived']]\n",
    "\n",
    "df = pd.concat([df,pd.get_dummies(df['Pclass'], prefix='Pclass',dummy_na=True)],axis=1).drop(['Pclass'],axis=1)\n",
    "df = pd.concat([df,pd.get_dummies(df['Sex'], prefix='Sex',dummy_na=True)],axis=1).drop(['Sex'],axis=1)\n",
    "df = pd.concat([df,pd.get_dummies(df['SibSp'], prefix='SibSp',dummy_na=True)],axis=1).drop(['SibSp'],axis=1)\n",
    "df = pd.concat([df,pd.get_dummies(df['Embarked'], prefix='Embarked',dummy_na=True)],axis=1).drop(['Embarked'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass_1.0</th>\n",
       "      <th>Pclass_2.0</th>\n",
       "      <th>Pclass_3.0</th>\n",
       "      <th>Pclass_nan</th>\n",
       "      <th>Sex_female</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Sex_nan</th>\n",
       "      <th>SibSp_0.0</th>\n",
       "      <th>SibSp_1.0</th>\n",
       "      <th>SibSp_2.0</th>\n",
       "      <th>SibSp_3.0</th>\n",
       "      <th>SibSp_4.0</th>\n",
       "      <th>SibSp_5.0</th>\n",
       "      <th>SibSp_8.0</th>\n",
       "      <th>SibSp_nan</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "      <th>Embarked_nan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age     Fare  Survived  Pclass_1.0  Pclass_2.0  Pclass_3.0  Pclass_nan  \\\n",
       "0  22.0   7.2500         0           0           0           1           0   \n",
       "1  38.0  71.2833         1           1           0           0           0   \n",
       "2  26.0   7.9250         1           0           0           1           0   \n",
       "3  35.0  53.1000         1           1           0           0           0   \n",
       "4  35.0   8.0500         0           0           0           1           0   \n",
       "\n",
       "   Sex_female  Sex_male  Sex_nan  SibSp_0.0  SibSp_1.0  SibSp_2.0  SibSp_3.0  \\\n",
       "0           0         1        0          0          1          0          0   \n",
       "1           1         0        0          0          1          0          0   \n",
       "2           1         0        0          1          0          0          0   \n",
       "3           1         0        0          0          1          0          0   \n",
       "4           0         1        0          1          0          0          0   \n",
       "\n",
       "   SibSp_4.0  SibSp_5.0  SibSp_8.0  SibSp_nan  Embarked_C  Embarked_Q  \\\n",
       "0          0          0          0          0           0           0   \n",
       "1          0          0          0          0           1           0   \n",
       "2          0          0          0          0           0           0   \n",
       "3          0          0          0          0           0           0   \n",
       "4          0          0          0          0           0           0   \n",
       "\n",
       "   Embarked_S  Embarked_nan  \n",
       "0           1             0  \n",
       "1           0             0  \n",
       "2           1             0  \n",
       "3           1             0  \n",
       "4           1             0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "\n",
    "data=df.drop(['Survived'],axis=1)\n",
    "target = df['Survived']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(data.values, target.values, test_size=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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CdrY0ZsgFLCmoaoaI3AnMwE03naCqK0VkOLBQVacB9wHjRGQwbtB5oNpqJmXWzZMW8NP6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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdab0c02c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf = XGBClassifier()\n",
    "clf.fit(X_train,y_train)\n",
    "\n",
    "y_preds = clf.predict_proba(X_test)\n",
    "preds = y_preds[:,1]\n",
    "\n",
    "# fpr means false-positive-rate\n",
    "# tpr means true-positive-rate\n",
    "fpr, tpr, _ = metrics.roc_curve(y_test, preds)\n",
    "\n",
    "auc_score = metrics.auc(fpr, tpr)\n",
    "\n",
    "plt.title('ROC Curve')\n",
    "plt.plot(fpr, tpr, label='AUC = {:.3f}'.format(auc_score))\n",
    "\n",
    "# it's helpful to add a diagonal to indicate where chance \n",
    "# scores lie (i.e. just flipping a coin)\n",
    "plt.plot([0,1],[0,1],'r--')\n",
    "\n",
    "plt.xlim([-0.1,1.1])\n",
    "plt.ylim([-0.1,1.1])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "importance_data = sorted(list(zip(data.columns,clf.feature_importances_)),key=lambda tpl:tpl[1],reverse=True)\n",
    "\n",
    "xs = range(len(importance_data))\n",
    "labels = [x for (x,_) in importance_data]\n",
    "\n",
    "ys = [y for (_,y) in importance_data]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Nr4msV37dasHnLEzRUj5nsW6VMl1yN5HgYtlKmTYRWiusaX79xprm10T2rJlI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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdab0a8cb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "plt.bar(xs,ys,width=0.5)\n",
    "plt.xticks(xs,labels,rotation=45,ha='right')\n",
    "\n",
    "plt.gca().grid(True)\n",
    "# select both y axis and x axis\n",
    "gridlines = plt.gca().get_xgridlines() + plt.gca().get_ygridlines()\n",
    "# choose line width\n",
    "line_width = 0.7\n",
    "\n",
    "plt.ylabel('relative feature importance')\n",
    "\n",
    "for line in gridlines:\n",
    "    line.set_linestyle(':')\n",
    "    line.set_linewidth(line_width)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_columns_names = ['Pclass','Sex','SibSp','Embarked']\n",
    "\n",
    "for column_name in categorical_columns_names:\n",
    "    \n",
    "    all_values_sum = 0\n",
    "    \n",
    "    for key,value in list(importances_dict.items():\n",
    "        if key.startswith(column_name):\n",
    "            all_values_sum += importances_dict[key]\n",
    "            del(importances_dict[key])\n",
    "            \n",
    "    importances_dict[column_name] = all_values_sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "importance_data = sorted(list(importances_dict.items()),key=lambda tpl:tpl[1],reverse=True)\n",
    "\n",
    "xs = range(len(importance_data))\n",
    "labels = [x for (x,_) in importance_data]\n",
    "\n",
    "ys = [y for (_,y) in importance_data]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZYd7KQkTWAMdU9SUishlYo6oDqxNafdA6aOfOFOx2a2hoMB1CqNjs59zMMG8z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\n3V6fJgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdab0a6fc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "plt.bar(xs,ys,width=0.5)\n",
    "plt.xticks(xs,labels,rotation=45,ha='center')\n",
    "\n",
    "plt.gca().grid(True)\n",
    "# select both y axis and x axis\n",
    "gridlines = plt.gca().get_xgridlines() + plt.gca().get_ygridlines()\n",
    "# choose line width\n",
    "line_width = 0.7\n",
    "\n",
    "plt.ylabel('relative feature importance')\n",
    "\n",
    "for line in gridlines:\n",
    "    line.set_linestyle(':')\n",
    "    line.set_linewidth(line_width)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Global TF Kernel (Python 3)",
   "language": "python",
   "name": "global-tf-python-3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
